Time Path — Prior Evidence

• Attention diversion slows tasks: When cognitive resources shift to phones, shoppers neglect lists/goals, prolonging trips (Block & Morwitz 1999; Inman, Winer, & Ferraro 2009).
• Longer dwell → larger baskets: Empirical store‑tracking work links minutes in aisle to higher spend (Sorensen 2003).

Hypothesis 1

H1a. Mobile phone use increases total time spent in the store.
H1b. More time in store increases purchases.

Shelf‑Attention Path — Prior Evidence

• Pause = extra visual sweep: Phone‑induced stops grant shoppers seconds to scan nearby products (Atalay, Bodur, & Bressoud 2017).
• Visual‑attention work shows even micro‑fixations raise product awareness (Anstis 1998; Pieters & Wedel 2012).

Hypothesis 2

H2a. Mobile phone use increases shelf attention.
H2b. Greater shelf attention increases purchases.

Loop‑Diversion Path — Prior Evidence

• Shoppers follow spatial scripts around the perimeter; distraction prompts deviations/back‑tracking (Bower, Black, & Turner 1979).
• Deviations expose consumers to otherwise unseen SKUs, boosting unplanned buys (Hui et al. 2013).

Hypothesis 3

H3a. Mobile phone use increases loop diversion.
H3b. Greater loop diversion increases purchases.

Mediation Test — Integrating the Paths

• Limited‑capacity theory (Repovš & Baddeley 2006) predicts that time, attention, and path shifts collectively transmit distraction effects to spending.

Hypothesis 4

H4. Time in store, shelf attention, and loop diversion jointly mediate the positive relationship between phone use and purchases.

Study Design

• Context: Real‑life grocery shopping trips in four suburban Stockholm supermarkets (~36 140 ft² each).
  
• Method: Eye‑tracking field study — portable Tobii Pro glasses record visual field & gaze.
  
• Purpose: Capture natural shopper behavior; maximize ecological validity vs. lab scenarios.

Sampling & Data Collection

• Recruitment: Research staff at store entrances (Mon–Sun, 09:00–17:00).
  
• Intercept every passer‑by; offer coupon incentive; no mention of phone focus.
  
• Initial recruits: 393 shoppers → 359 with usable video → 294 final (completed exit survey).

Sample Characteristics

• n = 294 | Age 18‑73 (M = 41.5).
  
• Gender: 39.5 % female.
  
• No demographic differences across stores or phone‑use groups.

Measures

Stimuli & Equipment

• Stimulus set: The live store environment (thousands of SKUs).
  
• Equipment: Tobii Pro Glasses; Figure 1 illustrates visual field & fixations.

Figure 1

Mediation Model

Models Tested in Study 1

Study 1 Results (Key Paths)

• Mobile users spent +4.6 min in store (p < .001).
  
• Shelf fixations +17 % (p = .02).
  
• Loop diversions +0.97 (p < .001).
  
• Basket value +120 SEK (~$14) (p = .007).
  
• Indirect effects via all three mediators significant.

Study 2 — Field Experiment

Purpose & Design

• Goal 1: Replicate Study 1 findings with fresh sample.
  
• Goal 2: Eliminate self‑selection bias via random assignment to phone‑use vs no‑use condition.
  
• Goal 3: Directly measure distraction to test limited‑attention theory (4‑item, α =.90).

Sampling & Random Assignment

• Setting: Two suburban grocery stores (same chain).
  
• Recruitment: Every 5th passer‑by at a predefined entrance point; scratch‑off lottery ticket incentive.
  
• Eligibility: Must carry a mobile phone (4 shoppers excluded).
  
• Sample: 121 recruited → 117 usable (24 h of eye‑tracking video).
  
• Randomization: Assigned to Phone‑Allowed (n = 53) or Phone‑Prohibited (n = 64) groups.

Sample Characteristics

• n = 117 | Age 19‑80 (M = 42.9).
  
• Gender: 52.1 % female.
  
• No age, gender, or household‑size differences across conditions.

Measures

Manipulation Check & Compliance

• Non‑use group: 96.9 % obeyed (2 urgent calls).
  
• Use group: 96.2 % used phones (~0.74 min; 4.8 % of trip).
  
• Randomization + high compliance ⇒ strong internal validity.

Analysis Model

• Serial mediation: Phone condition → Distraction → (Time | Fixations | Diversion) → Purchases.
  
• PROCESS Models 4 & 6; 100 000 bootstrap iterations.

Key Results

Note: Age significantly moderates all three mediator paths; older shoppers show stronger effects.

Summary

• Experimental replication confirms causal impact of phone use on distraction‑driven behaviors and spending.
  
• Distraction is the first‑link mechanism; behavioral changes transmit the effect.
  
• Age, department, and phone‑activity type act as boundary conditions.

Results Summary by Hypothesis

(† = marginal, p < .10)

Theoretical Implications

• Extends limited‑attention theory: Shows distraction can increase rather than impair goal performance when the task is open‑ended (shopping).
  
• Links micro‑level gaze to macro sales: Connects eye‑tracking metrics to receipt data, bridging consumer psychology and retail analytics.
  
• Aging & distraction: Demonstrates greater susceptibility among shoppers > 32 yrs, enriching lifespan cognition research.
  
• Generalizable distraction mechanism: Parallel effects found for shopping‑with‑companions, hinting at a broader attentional conflict framework.

Integrated Discussion

• Distraction loop: Phone use → cognitive split → linger, look, wander → more items bought.
  
• Net‑positive experience: Satisfaction unchanged (Study 1) or only marginally lower (Study 2); benefits outweigh any friction.
  
• Spatial nuance: Fresh‑food departments gain most; checkout losses confirm mobile blinders boundary.

Managerial Implications

• Provide free Wi‑Fi & charging stations.
  
• Embed QR codes / digital shelf labels near high‑margin SKUs.
  
• Target older shoppers with phone‑friendly services (voice search).
  
• Reframe showrooming: in‑store phone use can lift spend.

Limitations & Future Research

• Grocery context; test department stores & electronics.
  
• Manual coding limits eye‑tracking granularity.
  
• Explore content of phone use (e.g., social vs price‑check).
  
• Investigate motionless contexts (checkout, deli counter).